Fractional frequency reuse schemes assigned to radio nodes in an LTE network

ABSTRACT

Arrangements disclosed here provide an LTE E-RAN employing a hierarchical architecture with a central controller controlling multiple LTE radio nodes (RNs). The RNs may be clustered within the small cell network. A fractional frequency reuse (“FFR”) scheme is provided that dynamically computes the FFR allocations at individual RNs and configures the corresponding schedulers within each RN to improve cell-edge users&#39; experience. Once an FFR pattern has been generated and frequencies allocated, UE throughput can be emulated to predict the resulting bit rates for each UE. Using the prediction, a scheduler emulation may be run to predict the behavior of the system. The results of each cell may then be collected to generate the performance of the entire system, which may in turn be used to generate a new or modified FFR pattern, or new or modified clustering. Optimization of the performance results in an optimized FFR pattern.

BACKGROUND

Operators of mobile systems, such as Universal Mobile TelecommunicationsSystems (UMTS) and its offspring including Long Term Evolution (LTE) andLTE-Advanced, are increasingly relying on wireless small cell radioaccess networks (RANs) in order to deploy indoor (as well as denseoutdoor) voice and data services to enterprises and other customers.Such small cell RANs typically utilize multiple-access technologiescapable of supporting communications with multiple users using radiofrequency (RF) signals and sharing available system resources such asbandwidth and transmit power.

Deployment of a large number of small cells can improve system-widecapacity in an area by providing cell-splitting gains. However, thesesystems result in unique challenges to a RAN operator. Users at celledges often suffer from inter-cell interference since the receivedsignal power from the serving cell is at the same level or even belowthe level of the received aggregated interference power from theadjacent cells. If the inter-cell interference is not well-handled, thecapacity benefits from the small cell deployments could come at the costof reliability and the general stability of the system. Reliability andstability of a RAN are often captured by an extensive set of KeyPerformance Indicators (KPIs) that essentially characterize the userexperience in such a network. Unlike the deployments of macro cellnetworks, the deployments of small cell are more irregular in geometry.The shapes and sizes of the coverage areas of small cells can varygreatly. In addition, the load distribution between small cells is moreasymmetric when compared with that of macro cells, which cover muchlarger areas. These impose significant challenges for managing smallcell inter-cell interference and require techniques to take all theirregularities in geometry and load distribution into consideration aspart of the small cell network design. Specifically this meansinterference management schemes need to be designed differently. Somekey focus areas for the design are i) scalability (can support largenumber of small cells) and ii) stability (autonomously account fordifferent performance requirements/network conditions)

There are several approaches that may be used to reduce the influence ofinter-cell interference. For example, one approach is to employ afrequency reuse pattern and by that, avoiding usage of the samefrequency bands at adjacent cells. A drawback of this approach is thatonly a small fraction of the frequency resources (equal to the reusefactor) may be used in each cell, while preferably one would like toreuse a significant part of the whole available frequency spectrumwithin every cell. Another approach to improve the spectral efficiencyin cellular systems is a “fractional frequency” approach, which dividesthe frequency resource into two parts or more. The first part is usedfor the edge of cell regions, while the second part is used for theregions closer to the radio node. The first part is used with adesignated reuse factor, appropriate for the cell edges where users aremore vulnerable to interference due to their reduced signal power. Thesecond part (covering the inner part of the cell), however, can be usedwith a higher reuse factor because the Signal to Interference and NoiseRatio (SINR) is higher in this part of the cell in view of both thestronger desired signal and the larger distance from the interferers. Anexample of such approach, for example is to divide the availablechannels into 4 channels, three

of which are used in a reuse-3 pattern for covering the cell edgeregions, while the fourth channel is used in a reuse-1 manner for theinner regions of the cells.

Despite the use of the well-known aforementioned techniques for reducinginter-cell interference, additional improvements in cell-edge userperformance are desirable, particularly when small cells are employed.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY

Systems and methods according to implementations of principles disclosedhere provide an LTE E-RAN employing a hierarchical architecture with acentral controller or coordinator controlling multiple LTE radio nodes(RNs). The central controller or coordinator may form a portion of aservices node (SN) in communication with RNs that service user equipmentor UEs. These RNs may be configured as individual cells (i.e, withdifferent physical cell identities (PCIs)) within the small cellnetwork. The systems and methods provide for a fractional frequencyreuse (“FFR”) scheme with a central controller that dynamically computesthe FFR allocations at individual RNs and configures the correspondingindividual MAC schedulers within each RN to improve cell-edge users'experience or more generally to meet well-defined KPIs for the system.The systems and methods operate in a manner that is scalable so that itsperformance gains are retained even for large system deployments.

In an exemplary method, the central controller may obtain topologyinformation concerning the RNs. The central controller divides the RNsinto two or more clusters of RNs based on their topology and generates aFFR pattern for each cluster. The SN then allocates transmissionfrequencies to the RNs in each cluster in accordance with the respectiveFFR pattern that is generated for each cluster. A transmission frequencycould correspond to a sub-band, which is itself a set of resource blocks(RBs) as defined in 3GPP specifications TS 36.211, 36.212, 36.213.

Once an FFR pattern has been generated and frequencies allocated, thethroughput of UEs can be emulated to predict the resulting bit rates foreach UE. Using the resulting predicted bit rate, a scheduler emulationsuch as a proportional fairness (PF) scheduler emulation may be run topredict the behavior of the system, especially the performance metricsof UEs. The results of each cell may then be collected to generate thepredicted KPIs of the entire system. If the predicted KPIs indicate thata modification is warranted, such may be instituted, e.g., a new ormodified FFR pattern, or new or modified clustering, and a new set ofKPIs predicted. Optimization of the KPIs results in an optimized FFRpattern. The emulation may be repeated periodically or continuously toensure optimal resource allocation.

In general, various flavors of the schedulers may be used. A trulyproportional fair scheduler adjusts the achievable predictedinstantaneous rates to a user (or sometimes equivalently CQIs) byweights that capture the average rate achieved by that user so far. Around robin scheduler (RR) performs equal allocation to all the servedusers in a round robin fashion, i.e, one UE at a time in sequence.However, in real implementations, different users may be assigneddifferent QOS parameters. A QOS parameter could include a guaranteed bitrate, a maximum bit rate, or a guaranteed maximum delay or delaydistribution. Further each user may have multiple associated flows orradio bearers each with different QOS parameters. During operation, ascheduler can equivalently compute a certain weight associated with auser (or a traffic flow) which is a function of one or more of thesechannel, traffic and any other parameters. Some other parameters thatare unique to a user or the RN could impose restrictions, for example,control channel capacity constraints etc., can be used in the weightcomputation. Essentially, a QOS scheduler may be defined, as a moregeneral PF type scheduler, which computes generalized weights asmentioned and adjusts a predicted rate (or a KPI) metric to performscheduling, i.e, selects users and resources for transmission.

In one aspect, the invention is directed towards a method of optimizingfrequency allocation in a radio access network (RAN) that includes aplurality of RNs each associated with a cell and a SN operativelycoupled to the RNs, the method including: dividing the RNs in the RANinto a plurality of clusters of RNs;

generating a FFR pattern for each cluster; allocating transmissionfrequencies to the RNs in each cluster in accordance with the respectiveFFR pattern that is generated for each cluster; estimating bit rates ofone or more user equipments (UEs) associated with each cell; performinga scheduling emulation within each cell using the estimated bit ratesfor the one or more UEs; and combining the results of the schedulingemulations for each cell to obtain a systemwide performance metric, andif the systemwide performance metric is less than a target threshold,then repeating one or more of the dividing, generating, allocating,estimating, performing, and combining steps, until the systemwideperformance metric meets or exceeds the target threshold.

Implementations of the invention may include one or more of thefollowing. The estimating bit rates may include calculating a SlNR foreach UE; and using a SINR-to-bit-rate map to determine the bit rate foreach UE. The calculating a SINR may include using the geometry ortopology of the UEs to calculate the SINR, including using the geometryof the clusters to calculate the SINR. The estimating bit rates mayfurther include improving the estimation by using CQI data reported bythe UE or by using measurement data on an uplink reference signal, suchas where the measurement data includes a signal and subband specificinterference measurement. The FFR pattern in the systemwide performancemetric may refer to an uplink or a downlink transmission. The estimatingand performing may be performed for each cell by a services node. Thetransmission frequencies may be frequencies used for uplink or downlinktransmission. The systemwide performance metric may be selected from thegroup consisting of: cell packet throughput, 5% cell edge userthroughput, user throughput CDP, call drop ratio, call setup successrate, radio link failure rate, and handover delay. A further step mayinclude synchronizing the RNs, where the synchronizing includessynchronizing the control channels of the RNs, such as to within oneOFDM symbol. Following the system-wide performance metric meeting orexceeding the target threshold, transmission frequencies may beallocated to the RNs in each cluster in accordance with the respectiveFFR pattern that resulted in the metric meeting or exceeding the targetthreshold, and a step may be performed of scheduling within each RN toallocate transmission frequencies to the UEs served by the RN, and thescheduling may direct cell edge users to high reuse bands and cellcenter users to low reuse bands. The scheduling step may be at leastpartially based on OE-specific information, including CQI, load, and QoSparameters. The systemwide performance metric may include the throughputof a cell edge user, and the repeating the generating step may includemodifying the size of the center band until the throughput of the celledge user meets or exceeds the threshold. The scheduling step mayinclude PF scheduling, round-robin scheduling, or QOS based scheduling.The scheduling emulation may include PF scheduling emulation,round-robin scheduling emulation, or QOS based scheduling emulation.

In another aspect, the invention is directed towards a non-transitorycomputer readable medium, including instructions for causing a computingenvironment to perform the above method.

In another aspect, the invention is directed towards a radio node (RN)communicating with a plurality of user equipment (UE) in a radio accessnetwork, including: a processor; a first input for an FFR pattern; asecond input for data about communications performance of a plurality ofUEs associated with the RN; a scheduler module receiving data from thefirst and second inputs and configured to provide as output a schedulingof the plurality of UEs, such that the scheduler module is configured todirect a subset of the plurality of UEs corresponding to cell edge usersto high reuse bands at least in part based on data from the secondinput, and further directs a subset of the plurality corresponding tocell center users to low reuse bands.

Implementations of the invention may include one or more of thefollowing. The data from the second input may be selected from the groupconsisting of: CQI, load, and QoS parameters. The radio node may furtherinclude a synchronization module for receiving a signal from a SN, thesignal for synchronizing the control channel of the RN. The FFR patternmay be based on a plurality of parameters, each selected for a differentcluster of RNs within the radio access network. The scheduling modulemay be a PF scheduling module, a round-robin scheduling module, or a QOSbased scheduling module.

In another aspect, the invention is directed towards a method ofoptimizing transmission resource allocation in a radio access network(RAN) that includes a plurality of radio nodes (RNs) each associatedwith a cell and a services node operatively coupled to the radio nodes,the method including: obtaining at least one system-wide performancemetric representing operational performance of the RAN; if thesystem-wide performance metric is less than a target threshold, thenadjusting a system-wide FFR pattern used to allocate transmissionresources to the RNs until the system-wide performance metric meets orexceeds the target threshold, the system-wide FFR pattern including aplurality of cluster-based FFR patterns each being allocated to adifferent cluster of RNs, the RNs in the RAN being divided into aplurality of clusters.

Implementations of the invention may include one or more of thefollowing. The adjusting the system-wide FFR pattern may includeadjusting FFR scheme-related parameters used to generate the system-wideFFR pattern. The adjusting the system-wide FFR pattern may includeadjusting one or more of the cluster-based FFR patterns by adjusting atleast one operator-specified value selected from a plurality ofparameters which are used as input data, where the plurality ofparameters may include an FFR type specifying at least one criterion forallocating edge bands to the RNs in each cluster. The plurality ofparameters may further include a number of frequency resource blocksassigned to a center band allocated to the RNs in each cluster for useby UEs in a cell interior of each cell. The plurality of parameters mayfurther include a scheduling granularity of the center band specifying anumber of frequency resource blocks assigned to the center band whichare scheduled together. The plurality of parameters may further includea scheduling granularity of the edge band specifying a number offrequency resource blocks assigned to the edge band which are scheduledtogether. The FFR type may be selected from the group consisting ofuniform FFR and load-based FFR, where uniform FFR allocates differentedge bands of uniform size to each RN in a cluster and load-based FFRallocates to each RN in a cluster different edge bands having a sizedetermined in part on load information obtained from the RNs in eachcluster. The group may further consist of an FFR type in which a subsetof RNs in a given cluster share edge bands. The load information may beselected from the group consisting of a load of each RN in a cluster,the number of active UEs served by each RN in a cluster, and DE-specificinformation, where the DE-specific information is selected from thegroup consisting of RSRP, load, QoS, sub-band channel quality indicators(CQIs), buffer status or latencies, and current or past KPIs maintainedper RN or per UE. Adjusting the system-wide FFR pattern may includeadjusting RN clustering parameters, wherein the RN clustering parametersmay include a total number of clusters into which the RNs in the RAN areto be divided. The RN clustering parameters may include selection of aclustering algorithm used to divide the RNs into the plurality ofclusters. The transmission resources may be resources used for uplink ordownlink transmissions, or both. For at least one of the clusters, thecluster-based FFR pattern generated for uplink transmission may be thesame as the cluster-based FFR pattern generated for downlinktransmission. A cell associated with each RN may include a cell interiorand a cell edge and, for at least one of the clusters, a size of thecell interior or cell edge associated with a given one of the RNs forpurposes of uplink transmission is different from the size of the cellinterior or cell edge associated with the given RN for purposes ofdownlink transmission. The systemwide performance metric may be selectedfrom the group consisting of: cell packet throughput, 5% cell edge userthroughput, and user throughput CDF, call drop ratio, call setup successratio, radio link failure rate, and handover delay.

In another aspect, the invention is directed towards a non-transitorycomputer readable medium, including instructions for causing a computingenvironment to perform the above method.

In a further aspect, the invention is directed towards a services nodecontrolling a plurality of radio nodes (RNs) in a radio access network(RAN), the plurality of radio nodes communicating with a plurality ofuser equipment (UE) in the radio access network, including: a processor;and a performance evaluation module operatively associated with theprocessor, the performance module having an input for obtaining at leastone system-wide performance metric representing operational performanceof the RAN, the performance evaluation module being configured such thatif the system-wide performance metric is less than a target threshold,then adjusting a system-wide FFR pattern used to allocate transmissionresources to the RNs until the system-wide performance metric meets orexceeds the target threshold, the system-wide FFR pattern including aplurality of cluster-based FFR patterns each being allocated to adifferent cluster of RNs, the RNs in the RAN being divided into aplurality of clusters.

In yet another aspect, the invention is directed towards a method forobtaining system performance metrics for a mobile communications systemthat includes a radio access network (RAN) having a plurality of radionodes (RNs) each associated with a cell and services node operativelycoupled to the RNs, including: obtaining an FFR pattern for the RAN;receiving from UEs operational in the cells a measurement of signalstrength; based at least in part on the FFR pattern and the measurementof signal strength, emulating scheduling functionality performed by theRNs for allocating transmission resources to the UEs; based at least inpart on the emulation, determining at least one performance metric foreach of the cells in the RAN.

Implementations of the invention may include one or more of thefollowing. The method may further include determining, for differentlevels of interference, an SINR for each of the UEs from the measurementof signal strength and the FFR pattern; and mapping each of the SINRs toa per-sub carrier bit rate, where emulating the scheduling functionalityis performed using the per-subcarrier bit rates. The emulating thescheduling functionality may be performed by the RNs and the determiningthe at least one performance metric may be performed by the servicesnode. The measurements of signal strength may include that of UE RSRPs.The at least one performance metric may be selected from the groupconsisting of a cell packet throughput, 5% cell-edge user throughput anda user throughput cumulative distribution function (CDF), call dropratio, call setup success rate, radio link failure rate, and handoverdelay. The method may further include aggregating the at least oneperformance metric for each of the cells to obtain at least onepredicted key performance indicator (KPT) for the mobile communicationssystem. The method may further include receiving from the UEs,operational in the cells, channel quality information (CQI), whereemulating the scheduling functionality performed by the RNs forallocating transmission resources to the UEs is also based at least inpart on the CQI. The mapping may account for MIMO rank adaptation inwhich a number of transmission streams to the UEs adaptively changesaccording to a channel environment. The mapping may be derived by anempirical approximation of SNR to bit rate relationship using networkoperational data. The network operational data may include pairs of SNRand rate measurements at individual UEs collected system-wide fordownlink transmissions. The network operational data may also includepairs of SNR and rate measurements at individual RNs corresponding totransmissions from different UEs collected system-wide for uplinktransmissions. The FFR pattern for the RAN may be a system-wide FFRpattern that includes a plurality of FFR patterns that are eachassociated with a different subset of the RNs, each defining a clusterof RNs, where each cluster is selected such that interference betweenRNs within a cluster is greater than interference between RNs indifferent clusters. The transmission resources allocated to the UEs bythe RNs may include frequency resource blocks and/or a transmission timeinterval. The scheduling functionality may employ a proportionalfairness scheme, a round-robin scheduling scheme, or a QOS basedscheduling scheme. The allocated transmission resources may be resourcesfor uplink transmission or downlink transmission.

In yet another aspect, the invention is directed towards anon-transitory computer readable medium, including instructions forcausing a computing environment to perform the above method.

In yet another aspect, the invention is directed towards a services nodecontrolling a plurality of radio nodes (RNs) in a radio access network(RAN), the plurality of radio nodes communicating with a plurality ofuser equipment (UE) in the radio access network, including: a processor;a UE SINR calculator module operatively associated with the processorfor determining, for different levels of interference, an SINR for eachof the UEs from (i) a measurement of signal strength received from theUEs and/or (ii) a measurement of signal strength from the UEs measuredon uplink reference signals and (iii) an FFR pattern used to allocatetransmission frequencies to the RNs; an SJNR bit rate mapping moduleoperatively associated with the processor for mapping each of the SINRsto a per-subcarrier bit rate; and a scheduler emulation moduleoperatively associated with the processor for emulating schedulingfunctionality performed by each of the RNs for allocating transmissionresources to the UEs based at least in part on the per-subcarrier bitrates.

Implementations of the invention may include one or more of thefollowing. The SINRs and the FFR pattern may correspond to downlink oruplink transmission from RNs to the UEs. The measurements of signalstrength may include UE RSRPs, and may further include measurement ofreceived reference signal received power (RSRP) and interference poweron uplink sounding reference signals (SRS) in LTE systems. The servicesnode may further include a system performance metric prediction modulefor determining at least one performance metric for each of the cells inthe RAN based at least in part on an output from the scheduler emulationmodule. The at least one performance metric may be selected from thegroup consisting of a cell packet throughput, 5% cell-edge userthroughput and a user throughput cumulative distribution function (CDF).The system performance metric prediction module may be furtherconfigured to aggregate the at least one performance metric for each ofthe cells to obtain at least one predicted key performance indicator(KPI) for the mobile communications system. The scheduler emulationmodule may be further configured to emulate the scheduling functionalityperformed by the RNs for allocating transmission resources to the UEsusing CQI received from the UEs. The SINR bit rate mapping module may befurther configured to perform the mapping while accounting for MIMO rankadaptation in which a number of transmission streams to the UEsadaptively changes according to a channel environment. The FFR patternfor the RAN may be a system-wide FFR pattern that includes a pluralityof FFR patterns that are each associated with a different subset of theRNs, each defining a cluster of RNs, where each cluster is selected suchthat interference between RNs within a cluster is greater thaninterference between RNs in different clusters. The transmissionresources allocated to the UEs by the RNs may include frequency resourceblocks and/or a transmission time interval. The scheduler emulationmodule may employ a proportional fairness scheme, a round-robinscheduling scheme or a QOS based scheduling scheme.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative mobile telecommunications environment inwhich the present LTE FFR improvement may be practiced;

FIG. 2 shows details of an EPC (Evolved Packet Core) and E-UTRAN(Evolved UMTS Terrestrial Radio Access Network where UMTS is an acronymfor Universal Mobile Telecommunications System) arranged under LTE withwhich a small cell network may interoperate;

FIGS. 3A and 3B illustrate exemplary FFR schemes that usenon-overlapping RBs for cell-edge UEs of adjacent eNBs. FIG. 3Aillustrates an allocation of frequencies in a frequency reuse onescheme, where all frequencies have a frequency reuse one scheme. FIG. 3Bshows an exemplary FFR scheme that allocates dedicated frequency bands(edge bands) for each cell to create interference free orless-interfered bands for cell-edge users.

FIG. 4 illustratively shows a simplified functional block diagram ofillustrative hardware infrastructure for a SN, as well as illustrativeRNs, which may be utilized to implement the present principles.

FIG. 5 illustrates a set of RN s in a RAN which is divided into threeclusters;

FIG. 6 illustrates various exemplary types of FFR schemes according topresent principles;

FIG. 7 is a flowchart illustrating one exemplary method for establishinga mobile communications network that includes a small cell RANcontrolled by a SN in an environment such as a building, residence orthe like.

FIG. 8 illustrates a three story building in which a small cell networkaccording to present principles may be deployed;

FIGS. 9a and 9b illustrate exemplary results of a clustering processperformed on the RNs in the small cell network of FIG. 8.

FIG. 10 illustrates the impact of synchronization of control channels bythe RNs.

FIG. 11 illustrates improved throughput with synchronization.

FIG. 12 schematically illustrates an exemplary SN according to presentprinciples.

FIG. 13 schematically illustrates an exemplary scheduler emulationmodule according to present principles.

FIGS. 14 and 15 illustrate emulation verification for user throughputcumulative distribution function (CDF) as a KPI (FIG. 14) and an edge,median and mean user throughput as KPIs (FIG. 15).

FIG. 16 is a schematic diagram of an exemplary RN according to presentprinciples.

FIG. 17 is a flowchart illustrating a method according to presentprinciples.

FIGS. 18-21 illustrates various KPis which may be employed foroptimization, further indicating performance improvement in systemsemploying FFR.

FIGS. 22A and 22B show how exemplary FFR schemes can be employed tocause a soft partitioning between center and edge users. FIG. 22Aillustrates sub-band partitioning without FFR, and FIG. 22B illustratessub-band partitioning with FFR according to present principles.

FIG. 23 illustrates a macro cell in proximity with an E-RAN.

Like reference numerals indicate like elements in the drawings. Elementsare not drawn to scale unless otherwise indicated.

DETAILED DESCRIPTION

FIG. 1 shows an illustrative mobile telecommunications environment 100in which the present invention may be practiced. The mobiletelecommunications environment 100, in this illustrative example, isarranged as an LTE system as described by the Third GenerationPartnership Project (3GPP) as an evolution of the GSM/UMTS standards(Global System for Mobile communication/UMTS). It is emphasized,however, that the present principles described herein may also beapplicable to other network types and protocols. For example, othernetwork types and protocols that may be employed include, withoutlimitation HSPA, LTE, CDMA2000, GSM, IEEE 802.11/Wi-Fi, 802.16 etc. or amixture of technologies such as with a multi-standard radio (MSR) node(e.g., LTE/HSPA, GSM/HS/LTE, CDMA2000/LTE, etc).

The environment 100 includes an enterprise 105 in which a small cell RAN110 is implemented. The small cell RAN 110 includes a plurality of RNs1151 . . . 115 _(N). Each RN 115 has a radio coverage area (graphicallydepicted in the drawings as a hexagonal shape) that is commonly termed asmall cell. A small cell may also be referred to as a femtocell, orusing terminology defined by 3GPP as a Home Evolved Node B (HeNB). Inthe description that follows, the term “cell” typically means thecombination of a RN and its radio coverage area unless otherwiseindicated. A representative cell is indicated by reference numeral 120in FIG. 1.

The size of the enterprise 105 and the number of cells deployed in thesmall cell RAN 110 may vary. In typical implementations, the enterprise105 can be from 50,000 to 500,000 square feet and encompass multiplefloors and the small cell RAN 110 may support hundreds to thousands ofusers using mobile communication platforms such as mobile phones,smartphones, tablet computing devices, and the like (referred to as“user equipment” (UE) and indicated by reference numerals 1251-N in FIG.1). However, the foregoing is intended to be illustrative and thesolutions described herein can be typically expected to be readilyscalable either upwards or downwards as the needs of a particular usagescenario demand.

In this particular illustrative example, the small cell RAN 110 includesone or more SNs (represented as a single SN 130 in FIG. 1) that manageand control the RNs 115. In alternative implementations, the managementand control functionality may be incorporated into a RN, distributedamong nodes, or implemented remotely (i.e., using infrastructureexternal to the small cell RAN 110). The RNs 115 are coupled to the SN130 over a direct or local area network (LAN) connection (not shown inFIG. 1) typically using secure IPsec tunnels. In alternativeimplementations, the connection between the RNs 115 and SN 130 may beover a wireless link or wireless local area network (W-LAN) connection(not shown in FIG. 1). The SN 130 aggregates voice and data traffic fromthe RNs 115 and provides connectivity over an IPsec tunnel to a securitygateway SeGW 135 in an Evolved Packet Core (EPC) 140 network of a mobileoperator. The EPC 140 is typically configured to communicate with apublic switched telephone network (PSTN) 145 to carry circuit-switchedtraffic, as well as for communicating with an external packet-switchednetwork such as the Internet 150.

The environment 100 also generally includes Evolved Node B (eNB) basestations, or “macro cells”, as representatively indicated by referencenumeral 155 in FIG. 1. The radio coverage area of the macro cell 155 istypically much larger than that of a small cell where the extent ofcoverage often depends on the base station configuration and surroundinggeography. Thus, a given UE 125 may achieve connectivity to the network140 through either a macro cell or small cell in the environment 100.

Along with macro cells 155, the small cell RAN 110 forms an accessnetwork, i.e., an Evolved UMTS Terrestrial Radio Access Network(E-UTRAN) under 3GPP as represented by reference numeral 205 in FIG. 2.As shown, there is no standards defined centralized controller (similarto a Radio Network Controller (RNC) in UMTS/UTRAN) in the E-UTRAN 205,hence an LTE network architecture is commonly said to be “flat.” Themacro cells 155 are typically interconnected using an X2 interface andto the EPC 140 by means of an SI interface. More particularly, the macrocells are connected to the MME (Mobility Management Entity) 210 in theEPC 140 using an S1-MME interface and to the S-GW (Serving Gateway) 215using an S1-U interface. An SS interface couples the S-GW 215 to a P-GW(Packet Data Network Gateway) 220 in the EPC 140 to provide the UE 125with connectivity to the Internet 150. A UE 125 connects to the KNs 115over an LTE-Uu interface.

The SeGW 135 is also connected to the MME 210 and S-GW 215 in the EPC140 using the appropriate S 1 connections. Accordingly, as each of RNs115 in the small cell RAN 110 is operatively coupled to the SN 130 (asrepresentatively shown by lines 225), the connections from the RNs 115to the EPC 140 are aggregated to the EPC 140. Such aggregation preservesthe flat characteristics of the LTE network while reducing the number ofS1 connections that would otherwise be presented to the EPC 140. Thesmall cell RAN 110 thus essentially appears a single eNB 230 to the EPC140, as shown.

The LTE air interface uses Orthogonal Frequency Division Multiplexing(OFDM) for enhanced throughput and spectral efficiency. The airinterface has a Transmission Time Interval (TTI) of 1 ms (also referredto as a subframe) along with other features to lower latency. Theprimary element used in scheduling transmissions is a RB, and RBs makeup subframes which in turn make up radio frames. Each subframe includesboth control regions and data regions that are multiplexed intime-domain, in frequency domain, or in both time and frequency domains.In one method, FFR schemes are used over the data part (referred to asPOSCH and PUSCH region in LTE) of the subframe, where the availableresource can be divided into multiple sub-bands over the wholebandwidth.

One of the most significant issues in a multi-cell deployment is theperformance of user equipment at cell edge. For example, a UE at a celledge experiences significant interference from the downlinktransmissions of adjacent eNBs in decoding the downlink transmissionsfrom its own serving cell. As a result, the cell-edge spectralefficiency is significantly poorer than that in the interior of thecell. The LTE standard has introduced several coordination mechanisms toimprove cell-edge performance.

In one such mechanism, a FFR scheme, uses non-overlapping RBs forcell-edge UEs of adjacent eNBs as illustrated in FIGS. 3a and 3b . InFIG. 3a all frequencies have a frequency reuse one scheme. The FFRscheme in FIG. 3b allocates dedicated frequency bands (edge bands) foreach cell to create interference free or less-interfered frequency bandsfor cell-edge users. It also manages to maintain the average systemperformance by using a common center band (with a frequency reuse factorof one) for cell-center users. The FFR scheme can be viewed as acombination of the frequency reuse one scheme with higher (>1) frequencyreuse factor schemes. As shown in FIG. 3b , the less interfered centerusers of all cells utilize the same frequency (center band) while theheavily interfered edge users in different cells are directed to nonoverlapped dedicated frequency bands (edge band) to create separationwith each other. The scheduling decision is made based on DE-specificinformation such as sub-band Channel Quality Information (CQI), load,and Quality of Service (QoS). The FFR scheme described below is scalableand has the ability to autonomously optimize and select the desired FFRpattern for each RN depending on the various performance targets, e.g.,KPIs that are employed.

The FFR scheme described herein may be employed with a centralcoordinating entity, e.g., hosted on a SN, that dynamically computes theFFR allocation and accordingly configures the individual MAC schedulerswithin each RN. Use of a central coordinating entity provides a numberof benefits, in particular, frequency allocations can be calculated muchmore efficiently and rapidly as opposed to distributed algorithms inwhich a mesh of eNBs would exchange messages and generally take muchgreater computational time to converge to a global optimal solution.

In the centrally coordinated FFR scheme described herein the SN is ableto collect information and measurements from the RNs and act on it atthe same time. The information may include the topology of the RNs, thegeometry of individual UEs, the distance between UEs and each RN, thenumber of UEs served by individual RNs, the load of the UEs and RNs, andQoS requirements of the UEs. The topology of the RNs and the geometry ofthe UEs may be obtained from measurements of signal strength (e.g. RSRPin LTE, CPICH RSCP in HSPA, GSM BCCH RSSI, CDMA2000 1×RTT PilotStrength, CDMA2000 HRPD Pilot Strength etc) at the RNs (RN sniffing) andUEs respectively. Here topology specifically refers to RN-RN pairwisemetrics, whereas geometry refers to UE to RN pair-wise metrics, and RSRPor RF pathless are examples.

The centrally coordinated FFR scheme employs two primary processes:

1. A RN scheduling process, which runs at each RN.

2. An FFR pattern generation process, which runs at the SN.

The FFR pattern generation process performed by the SN will be discussedfirst, in FIGS. 4-11. This is followed by a discussion of using thegenerated FFR pattern in a PF scheduler emulation with appropriatefeedback to determine the optimized clustering and FFR pattern, givendesired KPI metrics, as well as RN scheduling, in FIGS. 12-23.

FFR pattern generation modules may take into account the behavior of theRN scheduling process in some of the methods. An FFR pattern at a RN maybe defined essentially as i) the set of sub-bands (or transmissionfrequencies) that it is allowed to transmit on and ii) the set offrequencies that it is precluded from transmission. More generally, anFFR pattern could describe the power constraints/restrictions on one ormore sub-bands. The methods described herein can be adapted to this moregeneral definition. In practice, many of the benefits can be obtainedfrom using either a full power or zero power transmission in individualsub-bands. Accordingly, for purposes of illustration only and not as alimitation on the subject matter disclosed herein the followingdiscussion will focus on FFR patterns that operate in this binarymanner.

The set of frequencies in which transmission is allowed and the set offrequencies in which transmission is precluded may change each subframeaccording to a time-domain pattern, in which case the FFR patternspecifies both frequencies and subframes in which transmission isallowed or precluded. The time-domain pattern may be generated such thatcell edge resources can be shared closer to a desired ratio.Alternatively, the time-domain pattern may reflect constraints imposedby neighboring macro cells.

Referring now to the schematic layout of FIG. 4, an exemplary SN 320 isillustrated in radio communication with a number of cells 345 ₁-345 ₄.Each cell 345 _(i) includes a RN 335 _(i), within. Each RN 335 _(i),includes at least one MAC scheduler 340 _(i). The MAC scheduler 340_(i), is responsible for scheduling transmissions in both the uplink anddownlink: directions for the UEs it serves.

The SN 320 includes a processor 324 and a number of modules to carry outits desired functionality, including a clustering module 326 and an FFRmodule 330.

The clustering module 326 partitions the RNs controlled by the SN intomultiple clusters of RNs using the topology of the RNs as input. In oneimplementation an approximately fixed number of RNs is targeted percluster. The FFR process involves the selection of the center bandregion and an edge band region that is common across all clusters (andthe corresponding RNs). Within each cluster, FFR patterns are furtherallocated to the RNs, where the SN allocates the available edge bands toindividual RNs. Use of a clustering approach can completely eliminateintra-cluster interference and significantly reduce the inter-clusterinterference on the edge band regions.

The parameters which are used as inputs into the clustering module 326include the designated numbers of clusters and the cluster size. Eachcluster represents an interference group such that the RNs within thesame cluster interfere with each other more strongly than with RNs inother clusters. Such a definition of the interference relationship maybe expressed in an average sense. For example, an intra-cluster distancemetric may be minimized and inter-cluster distance metric may bemaximized for this purpose. Similar metrics related to absolute distancebased topology (ex: using X,Y,Z coordinates) or RF topology (pathloss orRSRPs between nodes) can also be used.

An example in which a set of RNs 360 associated with a common SN isarranged into three clusters 372, 374 and 376 is shown in FIG. 5. Thesolid lines between nodes indicate strong interference and dashed linesindicates weak interference. Interference between RNs that are notconnected by either a solid or dashed line is negligible.

Along with values for the parameter inputs of cluster size and thenumber of clusters, the inputs to the clustering module 326 include theinformation containing the topology of RNs as described, which can beexpressed in terms of their explicit positions (e.g., GPS type X, Ylocation+ floor information) and/or a relative metric such as pair-wiseRSRPs (i.e., topology) between RNs in an LTE system.

Hierarchical clustering algorithms, for example, “Group AverageAgglomerative Hierarchical Clustering” and “Ward's AgglomerativeHierarchical Clustering”, may be used to group RNs when only pair-wiseRSRP or path-loss values between RNs are available (i.e., the topologyinformation that is available when the network is booted and a neighborscan is completed). Other methods like “K-means” and “C-means fuzzyclustering” can be employed when the absolute metrics like RNspositions/locations are available. The use of such clustering techniquescan facilitate the FFR scheme's ability to scale up to accommodate alarge number of RNs robustly and effectively. This is achieved by usinga hierarchical approach to FFR pattern design using clustering as willbe further described.

After the RN s have been arranged into clusters, the intra-cluster FFRpattern generator 332 in the FFR module 330 generates various FFRpatterns for each cluster. The patterns are generated based oninformation obtained from the RNs within each cluster and values forvarious parameters that are input to the intra-cluster FFR patterngenerator 332. Such parameters which may be specified include the FFRtype, the size of the center band, the scheduling granularity of thecenter band and the scheduling granularity of the edge band. The FFRtype specifies the criterion to be used for allocating the edge bands tothe RNs in each cluster. Illustrative FFR types that may be employedwill be discussed below. The size of the center band specifies thenumber of frequency RBs contained in the center band. The schedulinggranularity of the center band divides the RBs in the center band intoRB groups which are to be scheduled together. RB groups may correspondto a sub-band or transmission frequency as described earlier, and maycomprise a set of contiguous or non-contiguous RBs. The size of the RBgroup depends on the granularity that is chosen. Likewise, thescheduling granularity of the edge band divides the RBs in the edgebands into RB groups which are to be scheduled together. Theintra-cluster FFR pattern generator 332 has the flexibility ofgenerating FFR patterns using different methods, which may be selectedby the particular information obtained from the RNs within each clusterand the values for the aforementioned parameters, when available.

The types of FFR schemes that may be employed can be broadly dividedinto two categories: uniform FFR and load-based FFR.

In uniform FFR, the edge bands are uniformly distributed among the RNsin a cluster. In other words, each RN is allocated edge bands having thesame size. This scheme requires minimal information from the RNs.

In a variation of the above method, a fixed FFR scheme may be used whichsimilarly does not rely on information from the RNs. However, theallocation of the edge bands need not be uniform. The fixed sizes withineach RN are determined based primarily on the topology i.e., RFenvironment and any known/expected RN usage. As an example, for RNs thatare known to be at the edge of the network and hence are needed tosupport ingress and egress of users, preferentially more edge bands maybe assigned. As another example, for RNs that are known to be inlocations where higher user density is expected (like conference roomsindoors), preferentially higher number of sub-bands may be assigned.

In load-based FFR, the size of the edge band that is allocated to eachRN is adjusted according to the load information collected from the RNs.The load information could be one or more of the load of the RN, thenumber of active UEs served by the RN, and UE-specific information suchas RSRP, load, QoS requirements, and sub-band channel quality indicators(CQIs), for example. They could also include information like bufferstatus or latencies or current or past KPJs maintained per RN or per UE.In one illustrative load-based FFR scheme, the edge band is allocatedwith a size in proportion to the number of active UEs associated witheach RN. Another example is to allocate the edge band in proportion tothe total expected aggregate data rate across the QoS bearers of the UE.A configurable weighted priority can also be used. Moreover, it is alsopossible that RNs having very few users, i.e., RNs which are lightlyloaded, may not be allocated any edge bands. In one method, a particulartraffic or usage or application type may be prioritized. As an example,a number of voice users at each RN may be used for deriving the FFRpatterns.

FIG. 6 illustrates some examples of different types of frequencyallocation schemes that may be employed. Each scheme is shown for acluster of 6 RNs denoted C1-C6. The frequency band is divided intotwelve sub-bands, which are shown along the rows for each RN in a givenscheme. Shaded sub-bands indicate sub-bands that are allocated for useby their respective RNs and unshaded sub-bands indicate sub-bands thatare not allocated for use by their respective RNs. In this way thecorresponding sub-band allocations for each scheme are illustrated.

The first two schemes, wide-band (WB) and sub-band (SB), are frequencyallocation schemes that do not employ FFR and are described as areference mode of operation. In particular, in the WB scheme frequencyselective diversity is not exploited among the UEs and the RNs in everycell occupy the entire available bandwidth and schedule UEs in afrequency agnostic way. In other words, they do not exploit thesub-band/frequency specific channel quality. Also, only a single UE istypically assigned a given transmission time interval (TTI). In the SBscheme, the frequency resources are allocated on a sub-band basis toexploit frequency selective diversity and multiple UEs are assigned agiven TTI. The second scheme improves performance of the system over theWB schemes in a rich multipath environment.

The final three frequency allocation schemes shown in FIG. 6, FFR withuniform allocation (FFR-UNI), load-based FFR (FFR-LD) and FFR UNI with 2RNs sharing an edge band (FFR-UNI LR2), are all FFR schemes.

The FFR-UNI and FFR-LD schemes respectively correspond to the uniformPFR and load-based PPR schemes discussed above. As shown, in the PPR-UNIscheme each RN is assigned a single, dedicated edge sub-band. Likewise,in the FPR-LD scheme different RNs are assigned a different number ofedge sub-bands, based on their respective loads. That is, the RNs areeach allocated a total edge band that does not overlap with other edgebands in the cluster, but the size of each total edge band may bedifferent from RN to RN. In this example, for instance, RNs C2, C4 andC5 are each allocated a single edge sub-band, whereas RN C3 is allocatedthree edge sub-bands.

The final PFR scheme shown in FIG. 6, FFR-UNI LR2, is an example of ascheme that may be used with either a uniform or a load-based FFRscheme. In this scheme multiple RNs (but not all the RNs in the samecluster) can share edge bands instead of allocating a dedicated edgeband to each RN. In this case the reuse ratio (which otherwise is equalto the number of RNs in the cluster) is lowered. RNs within the samecluster can be chosen, for example, to share an edge band based on theirinterference topology. The RNs in a cluster that interfere less witheach other, such as those RNs that receive low RSRPs from one another,may be selected to share the same edge band. In the example shown inFIG. 6 RNs CI and C2 share a common edge band (comprising two edgesub-bands), RNs C3 and C4 share a common edge band (comprising two edgesub-bands) and RNs C5 and C6 share a common edge band (comprising twoedge sub-bands). This specific variation of FFR scheme may be used toimprove the effective reuse ratio of FFR and hence the systemperformance. In one method, this scheme can be generalized to allowsharing of a different number of RNs (instead of a fixed value like 2)in each sub-band.

To avoid accumulating interference on the edge bands allocated to RNs indifferent clusters, the center bands of the FFR patterns of the clustersmay all be aligned with one another. In other words, for all clustersassigned to a given SN, the center bands may share the same frequencybands and have the same size. However, in some cases there may be a needfor flexibility to support different center band sizes in differentclusters if there are significant differences in cluster trafficpatterns or RF conditions. In these cases, the algorithm will thenassign the center sub-bands such that there is as much alignment aspossible, i.e., alignment may not be applied in a strict sense. Forexample if M1, M2, M3 are center bands for clusters C1, C2, C3, then theassignment of these sub-bands is such that M1⊂M2⊂M3. It is clear that inthis case, sub-bands in cluster 1 that are in M3, but are not in M1 maybe used as edge bands within cluster I (increasing edge region in C1),but may not receive as much protection from inter-cluster interference.

Referring again to FIG. 4, the system-wide FFR pattern tuning module 334of the FFR module 330 combines the FFR pattern of the individualclusters into a system-wide FFR pattern. Such combination methods do notfurther change the size of the center/edge sub-bands within eachcluster, but may readjust them to further improve SNR in the edge bandsby aligning edge bands of RNs from different clusters. To elaborate, theRNs in different clusters share edge bands as a result of theintra-cluster FFR patterns that have been assigned to them by theintra-cluster FFR pattern generator 332. As a result inter-clusteredge-band interference may arise, though intra-cluster interference iseliminated. The system-wide FFR pattern module 334 further adjusts ortunes the FFR pattern of certain example clusters to reduce thisinterference. For example, a selected RN, RN1, in cluster 1 may beassigned an edge band on which RN1 observes the lowest interference fromother clusters. Note that this step can be performed in severaldifferent ways. In one method, a sequential approach can be used, forexample, starting from a first cluster where RNs can be arbitrarilyassigned, and then moving on to second cluster and assigning RNs in eachsub-band such that interference from cluster 1 is minimized, and so on.In another method, a joint optimization approach can be used, where acertain KPI can be maximized over the possible assignments. An exampleof the KPI could be the sum rate over the clusters and over the edgebands. Another example could be maximizing the minimum rate.

A given system-wide FFR pattern generated by the FFR module 330 may beupdated at a rate that may depend on a variety of factors including, forexample, the rate at which channel conditions change, load variationsand the long term topology between cells. The dynamics of variations inthe system-wide FPR pattern can be determined according to theadditional gains that are realized. Tradeoffs in performance andcomplexity are factors that may be used to determine the updatefrequency. For example, the signaling load on the links between the SNand the RNs may be taken into account when determining a suitable updatefrequency.

An update to the PFR pattern generation may also be triggered byadditional events that arise from changes in the network. Examples ofsuch changes could include failure of one or more RNs, i.e, RN no longerable to transmit and serve UEs, bring-up of an RN, i.e., adding the sameto the network, and so on. In case of such a network event, some or allof the modules of the FFR algorithm may be affected. In one method,clustering may be performed simply deleting the RN from thecorresponding cluster or adding a new RN to the ‘closest’ cluster, wherethe closeness is defined based on a pairwise distance metric, e.g.,RSRP, RF Pathloss, etc., as defined earlier. Further FFR patterns may bereassigned within the corresponding cluster.

FIG. 7 is flowchart illustrating one exemplary method for establishing amobile communications network that includes a small cell RAN controlledby a SN in an environment such as a building, residence or the like. Themethod begins at block 500 when a series of RNs are distributedthroughout the environment. At block 510 the SN obtains topologyinformation of the RNs. At block 520 the SN divides the RNs into two ormore of clusters of RNs based on their topology. Next, at block 530 theSN generates a FFR pattern for each cluster and finally, at block 540the SN allocates transmission frequencies to the RNs each cluster inaccordance with the respective FFR pattern that is generated for eachcluster.

The same sequence of operations described above for downlinktransmission also can be used to assign uplink frequencies for use byeach cell. In this case cell center and cell edge frequencies are thoseused for transmission by UE and reception by one or more RNs in the RAN.In general, the assignment of cells to clusters and the FFR pattern foreach cell may be different for uplink and downlink transmission. Thesize of the cell center and cell edge region for uplink may differ fromdownlink to allow for a different tradeoff between cell edge coverageand cell center throughput.

For load-based FFR, different uplink and downlink load and QoSrequirements can be accommodated for by assigning different FFR patternsfor uplink and downlink. Alternatively, identical FFR patterns can beassigned for uplink and downlink transmission on the same cell to reducesignaling load or to allow for more frequent FFR pattern updates for thesame signaling load. In this case a combined uplink and downlink loadmetric is used for FFR pattern generation. For example, a weighted sumof uplink and downlink load.

Scheduling granularity may differ on uplink and downlink, and it may notbe possible to schedule the same set of frequencies on uplink anddownlink. For example, uplink cell edge resources can be allocated as acontiguous set of RBs to accommodate a single-carrier uplink physicallayer such as in LTE. On the downlink, cell edge resources can beallocated to align with the sub-bands used for UE sub-band CQIreporting.

For both uplink and downlink, cell edge interference represents thedegree to which clusters are separated from each other and is a measureof the quality of the clustering assignment. Each cell can keep track ofthe inter-cluster (cell edge) interference level and send periodicreports to the SN. The clustering algorithm can then use thesemeasurements to select from possible clustering assignments.

Example

FIG. 8 shows a building 400 having three floors 410, 420 and 430. Asmall cell RAN network is to be deployed for an enterprise in thebuilding. As shown, RNs 440 and UEs 450 are randomly placed over thefloors.

Clustering is performed in the RNs 440 using Ward's AgglomerativeHierarchical Clustering Algorithm. This algorithm is chosen as being asuitable clustering algorithm which can be used with a relative metricsuch as pair-wise RSRPs or associated RF pathloss (i.e., topology),which is used as the input to the algorithm. Since the RNs are deployedover 3 floors the number of clusters is set to three to check if thealgorithm autonomously separates RNs on different floors.

An illustration of the clustering results on a random drop modelling adeployment of radio nodes is given in FIG. 9a for a 10 dB penetrationloss between floors and in FIG. 9b for a 20 dB penetration loss betweenfloors. The results show that even with only a relative metric such aspair-wise RSRPs between RNs, the Ward's Agglomerative HierarchicalClustering Algorithm can sufficiently recognize and separate RNs ondifferent floors. The ‘errors’ in the case of 10 dB penetration lossmight occur on those RNs that are on the perimeters of floors. Two RNsare misplaced in clusters when the penetration loss is 10 dB, while RNson the same floors are grouped together correctly when the penetrationloss is 20 dB. This may be perfectly normal, and not an error as RNs areto be grouped based on their topology and not their physical floorlocation. In that sense, in an actual deployment, a clustering algorithmautonomously accounts for any variations in different indoordeployments.

In one method, a clustering algorithm may use any available sideinformation, in addition to autonomously measurable RF metrics, toimprove the performance of the algorithm. For example, GPS coordinatesand floor information can be used as the additional side information.These may in turn be available from a GPS receiver or some of thisinformation corresponding to each RN may be manually input/configured atthe SN or RNs.

Although the FFR schemes belong to frequency-domain interferencecoordination techniques, synchronization should still be considered.This is because that Physical Downlink Control Channel (PDCCH) in LTEoccupies the entire bandwidth (with a distributed allocation ofresources), and the FFR scheme is primarily employed for PhysicalDownlink Shared Channel (PDSCH). If RNs are not perfectly synchronizedwith each other, the PDCCH of a RN could interfere with the PDSCH ofnearby RNs even if FFR schemes are configured. An illustration of theimpact of synchronization is given in FIG. 10. For example, underfrequency domain duplex (FDD) mode and Type 1 frame structure [3GPP36.211], the duration of a sub-frame includes 14 OFDM symbols.Typically, the PDCCH occupies the first 3/14 fraction of the time of asub-frame, i.e., 3 OFDM symbols. Under FFR schemes, if RNs aresynchronized within an OFDM symbol, the performance loss when comparedwith the case of perfect synchronization is approximately bounded by1/11. This is because at most 1 out of 11 OFDM data symbols arecompromised by the interference from the PDSCHs of adjacent cells.However, the impact could be significant without synchronization asshown on the right in FIG. 10.

In one method, information related to synchronization is used in thedesign of FFR patterns. For example, such information could include theexpected or measured timing error statistics, where such timing error isthe difference in sub-frame start timing among RNs. In another example,physical control channel configuration information could be used in thedesign of FFR patterns. Such control configuration information couldinclude, but not limited to number of resources allocated to controlchannel (like number of OFDM symbols in the downlink), usage statisticsor load of the control channel region of individual RNs.

FIG. 11 illustrates a set of exemplary effects of synchronization onedge, median, and mean user throughput. As may be seen, FFR deploymentswhich are perfectly synchronized provide best performance, however thosewith synchronization within one OFDM symbols still provide a significantimprovement on edge user throughput because the loss when compared withthe perfect synchronization scenario is approximately bounded by 1/11.However, the worst case of no synchronization reduces the gains of FFR,in some cases significantly. The actual performance losses are somewherein between, and depends at least on the level of synchronizationachieved in practice and the control loading.

Below are described systems and methods according to present principlesin which the above-described components are employed in a scheduleremulation in order to determine optimum or optimized clustering as wellas FFR patterns. Once optimized, the parameters can be employed by a RNin scheduling.

FIG. 12 illustrates an exemplary SN 501. Various modules are shown, ashave been described. In particular, the SN 501 includes a clusteringmodule 454, which takes as an input the RN topology 464. Another moduleshown is an intra-cluster FFR pattern generation module 456, which takesas an input the RN load 466, e.g., the number of UEs serviced by the RN.Another module illustrated is a systemwide FFR pattern tuning module458, which also takes as an input the RN topology 464.

A scheduler emulation, e.g., using PF, round robin, or QOS, may then berun based on the generated and tuned PPR pattern and associatedclusters. For this purpose a PF scheduler emulation module 468 ishypothesized and included in the SN 501, such taking as an input theproposed and tuned FFR pattern, as well as the RN topology 464 andinformation about UEs, including potentially R SRPs and QoS. The PFscheduler emulation module 468 is employed to estimate systemperformance KPIs with a hypothetical FFR pattern in each RN. The outputis used to generate the targeted KPI metrics.

Once the scheduler emulation is run, a performance prediction module 472determines if the desired threshold KPI metrics are met. If so, theresult is an optimized or acceptable E-RAN FFR pattern 476. If not, atleast some of the steps of clustering and pattern generation may berepeated. In this way, the SN selects the FFR pattern that meets and/orimproves or otherwise optimizes the performance metrics. The keyperformance metrics may include the cell packet throughput, 5% cell edgeuser throughput, and user throughput CDF, as well as other appropriatemetrics. Another set of preferred metrics are related to handoverbetween cells including call drop ratio, call setup success rate, radiolink failure rate, handover delay etc. These are typically improved byimproving cell-edge performance. The selected FFR pattern that meets andoptimizes the targeted KPI metrics is distributed to individual RNs.

In more detail, in a wireless network optimization, system performanceis measured by a set of KPIs. Requirements on these KPIs are essentiallythe targets for the system design and operation. The FFR algorithmsdiscussed above ultimately serve this purpose. In one method, themeasurements and statistics collected from the UEs are used to predictthe KPIs by the noted emulation module included as part of the FFRalgorithm. In one example, the RSR Ps reported by the UEs to individualRNs are used in the emulation to predict the SNR achieved at the UE fordifferent FFR patterns. In a following step, the SNRs of all the servedUEs at an RN along with an appropriate scheduler model (e.g.,proportional fair (“PF”) or round robin or QoS based schedulers) areused to predict the achieved rate CDP in that RN. Such rate distributionfrom the RNs can be combined to obtain a system-wide rate distributionwhich maps to the predicted KPIs. The FFR patterns can then be tuned orselected across the system to satisfy the KPI requirements. In onemethod, the reported CQIs from the UEs are used to correct and improvethe SNR-to-rate mappings.

In one method, a feedback loop is used to ensure the KPI targets aremet. The KPIs can be measured over a network at the SN and/or the RNsand are input to the FFR algorithm module. One or more KPTs may beselected as the target KPIs and FFR patterns may be readjusted if thefeedback KPIs do not satisfy the requirements. The accuracy of anyprediction model is limited (in many cases a simple and straight-forwardmodel is preferred) and the feedback loop helps to correct for biasesthat arise in real system operation due to dynamic channel and trafficbehavior.

While certain values are discussed above, the KPIs could be in anycategories of accessibility, mobility, retainability, integrity andavailability and be any of the KPIs defined, for example as in 3GPPspecification TS 32.450. The relative priority of these KPIs could beselected as an additional input for FFR algorithm optimization.

Additional details of the PF scheduler emulation module 468 areillustrated in FIG. 13. The module 468 models functionalities andcapabilities within the E-RAN FFR scheme. The functionalityapproximately predicts the throughput of UEs associated with one or moreRNs for a given FFR pattern. Inputs to the PF scheduler emulation module468 include the FFR pattern 482 and the UEs RSRPs 484. Thegeometry/topology inputs (RSRPs 484) are employed in a UE sub-band SINRcalculation module 486 to calculate long-term SINRs for all UEs withvarious different interference hypotheses. In particular an interferencehypothesis corresponds to the ON/OFF setting of all the individual RNsfor a sub-band given an FFR patterns, in which case the totalinterference is essentially the interference from all the active/ON RNsnot including the serving RN. The ratio of received power from theserving RN to this interference results in the predicted SNR for thatsubband. That is, once the geometry and topology of the UEs isdetermined, as well as the FFR pattern, the same may be employed tocalculate approximate values of SINR for the various UEs.

Once the SINRs are calculated, the same may be employed in order toobtain information about bit rates of the UEs. One way of performingthis step is by a mapping, such as in the SINR-to-bit-rate mappingmodule 488 shown. A graph showing an exemplary mapping is illustrated bychart 496. The mapping functionality performed by the module 488 mayimplicitly take into account the rank adaptation. The mapping module 488converts the per-subcarrier SINR to an estimated long-termper-subcarrier bit rate. The mapping used may be obtained fromsimulations and can be replaced with OTA data if needed. It is furthernoted that the mapping module 488 may employ UE CQI information toimprove the prediction, if available, and/or the same may be used inlieu of a mapping.

A similar approach to scheduler emulation is also applicable to theuplink transmissions. For this case, the services node may additionallyrely on measurements at radio nodes on the uplink reference signals likesounding reference signals (SRS). Such measurements could include thesignal power measurements and the sub-band specific interferencemeasurements on the uplink. SINR estimates can be obtained from thesemeasurements, which can then be used for the scheduler emulation. Thedownlink UE RSRP measurements may also be used for uplink estimates(with the assumption of downlink/uplink reciprocity).

From the module 488, the predicted bit rates of the UEs associated withthe RN are obtained. This information is then communicated to a singlecell PF scheduler emulation module 492, which performs a PF scheduleremulation using the estimated sub-band rates of UEs to predict thebehavior of the PF scheduler. By predicting the behavior of the PFscheduler, the expected performance metrics of the UEs may be obtained.The results of each cell are collected together to generate thepredicted KPIs 494 of the entire system.

FIGS. 14 and 15 illustrate the results of an exemplary PF emulation. Theresults verify that the PF scheduler emulation can accurately track theperformance of different FFR patterns. In the illustrated figures, alayout is used having a single floor with 8-9 RNs and 80 UEs. Twodifferent FFR types are emulated: FFR-UNI LR2 and FFR-LD. Both patternshave center band sizes approximately 40% of the entire bandwidth. InFIG. 15, the emulation captures that FFR-UNI LR2 provides a highermedian user throughput and average user throughput and further that thecell edge user throughput is lower when compared with FFR-LD. Theseaspects are also captured in the user throughput CDF graph in FIG. 14.

Once the results of the scheduler emulation have led to an optimized FPRpattern and clustering scheme, the optimized FFR pattern may bedistributed to the RNs in the network, each of which is responsible forscheduling, a function that is expected to be performed generallyindependently of all other RNs. Referring to FIG. 16, a schematic of anRN is shown. A primary module that resides within an RN 600 is a singlecell PF scheduler 498. Inputs to this module 498 include the optimizedPPR pattern 476, as well as UE information 502 such as CQI, QoS, load,and the like. The output of the single cell PF scheduler module 498 is aset of scheduled UEs with appropriate modulation and coding schemes(MCS). The RNs 600 also determines users to be scheduled in atransmission time interval (TTI), as well as the corresponding allocatedRBs in frequency.

Generally, in most implementations of present principles, users aredivided within a cell into cell center users and cell edge users, withcell center users occupying low reuse bands, and cell edge usersgenerally transmitting within high reuse bands, while adapting todynamic channel and load variations. At least two methods may beemployed to accomplish this. In a first exemplary method, the FFR schemeexplicitly partitions users into cell center users and cell edge users.In a second exemplary method, the PF scheduler module 498 automaticallydirects the traffic of cell edge users to high reuse bands using thesub-band CQT information, which results in a “soft partition” of centerusers and edge users.

In more detail, separate downlink and uplink schedulers at each RN maybe employed to schedule transmissions on each cell according to theassigned FFR pattern. Cell edge resources can be used for transmissionto and from UEs that are geographically distant from the cell center orcan be used to provide increased spectral efficiency and/or lower packeterror rate for high priority messages or users, such as for handoverbetween cells or for UEs that require a guaranteed bit rate.

When cell edge resources are used for transmission to and from usersthat are geographically distant from the cell center, the UEs can bepartitioned into cell center and cell edge based on an absolutecriterion such as a wideband CQI threshold, an RSRP threshold, or athreshold based on the difference between the cell center and the celledge sub-band CQT. Alternatively, frequency-selective scheduling can beused to allocate cell center and cell edge resources directly to UEsbased on their sub-band CQI reports.

The MCS used for transmission by the cell or UE may be matched to thechannel conditions to achieve reliable communication. One challenge withFFR schemes is the fact that cell center and cell edge resources oftenexhibit different levels of interference. To account for this, theschedulers in each RN keep track of separate outer loop link adaptation(OLLA) biases for transmission on cell center and cell edge resources.The downlink scheduler computes separate cell center and cell edgebiases for each UE. The uplink scheduler computes and stores a singleOLLA bias for each UE, in addition to keeping track of the difference inreceived interference power between cell center and cell edge resources.When scheduling transmission to and from a UE, the appropriate OLLA biasis used to select the MCS based on whether the transmission uses cellcenter or cell edge resources.

FIG. 17 is a flowchart 650 of an overall method according to presentprinciples, the individual steps of which are described in greaterdetail above. A first step is the obtaining of UE and RN information(step 506). Such may include various information including RSRPs, load,topology, geometry, QoS, CQI, and the like. A next step is theclustering of the RNs (step 508). In this step the RNs are divided intoclusters, where such clustering is intended to be configured such thatthe interference between RNs in the same cluster is more significantthan the interference between RNs in different clusters.

A next step is the FFR pattern generation (step 512), where FFR patternsare generated for each cluster. The center bands of the FFR patterns ofall clusters may be aligned, and the combination of the FFR patterns ofall RNs forms the pattern for the entire system.

The step of scheduling emulation is then performed (step 514), this stepemploying the clusters in the proposed FFR patterns. This step estimatesthe sub-band bit rates for all UEs, and performs an emulation such as aPF emulation for each cell using the estimated bit rates.

The results are then combined to obtain systemwide performance metricsor KPIs which are then evaluated (step 516). If the systemwideperformance metrics do not meet or exceed threshold values, one or moreof the preceding steps may be repeated using adjusted clusteringparameters, adjusted FFR patterns, or the like. For example, if the edgeuser throughput does not meet the target, the size of the center bandmay be decreased. Once the systemwide performance metrics meet or exceedthreshold values, the determined FFR pattern may be deployed to theactual system (step 518). For example, the determined FFR pattern may bedistributed to the various RNs for use in their PF scheduling.

Certain exemplary results of systems and methods according to presentprinciples are illustrated in FIGS. 18-20. In particular, FIG. 18illustrates a significant increase in 5% cell edge throughput, which maybe one of the KPI metrics optimized, by the use of the disclosedclustering and FFR techniques. Another suitable KPI metric foroptimization includes the user throughput CDF, as illustrated in FIG.19. Not only does the CDF of the FFR samples exceed others, asignificant decrease in the percentage of users with low rates is seen.A qualitatively similar decrease in low-throughput users is illustratedby the table of FIG. 20. Conversely, FIG. 21 illustrates how 5% celledge user throughput increases with the proposed FFR schemes.

As noted above with respect to FIG. 16, the single cell PF scheduler inthe E-RAN FFR scheme may be employed to cause a soft partitioningbetween the center and edge users. An example of such soft partitioningis illustrated in FIGS. 22A and 22B, in which a system is shown withfive RNs, 18 UEs, and 10 sub-bands (SB). The FFR pattern employed hasfive edge bands (sub-bands 6-10) and each sub-band is dedicated to asingle RN. The blue, pink, yellow, green, and red areas correspond tothe portion of the time that the UEs in a cell with the highest, secondto highest, third-highest, second to lowest, and the lowest throughputare scheduled in each sub-band, respectively.

From the figure, it may be seen that without FFR, UEs in a cell sharethe resource evenly, while with FFR, the PF scheduler automaticallydirects the traffic of cell edge users to high reuse bands and cellcenter users to center bands. In this system, explicitly defining celledge and cell center users thresholds are avoided, which may beadvantageous in some implementations since tuning such thresholds addsadditional complication and is deployment-specific. It is further notedthat other UE-specific or RN-specific QoS or other waitingconsiderations may also be incorporated into the scheduling framework.

The soft partitioning of users to the cell-edge and cell-centersub-bands is facilitated by the availability or prediction of thesub-band (or transmission frequency) specific CQIs and using these in aproportional fair type scheduler as a predicted rate. Cell-edge usersthat benefit most from reduced interference in cell-edge bands arescheduled in those bands, as a natural result of the dynamics of theproportional fair scheduler. A proportional fair scheduler is expectedto schedule users in their “best” sub-bands most of the time.

FIG. 23 illustrates how systems and methods according to presentprinciples may be affected by the presence of near or adjacent macrocells. These methods in general can also be applied to any externalcells (like picas, micros, or isolated small cells or femtos, that arenot controlled by the services node). In the system 700 shown in thefigure, a macro cell 522 is near or adjacent (at least near enough tointerfere) with an E-RAN 524. In particular, the macro cell 522interferes with certain edge RNs 526, leaving other RNs 528 unaffected.

In this regard it is noted that in an enterprise RAN deployment with acentralized control/SN, macro cells can communicate with a single pointinstead of each of the small cells. This allows the SN to impose ICICrestrictions on “edge-cells” that are in the macro-interference regionso as to affect their efficiency only “as needed”. This is illustratedin the figure below. As shown, most of the RNs in the indoor network arenot affected due to penetration loss in the building. Further, the SNcan jointly configure ICIC schemes within the small-cell network takingsuch into account.

Various solutions are possible. In one, the macro cell 522 cancommunicate a first interference information, in particular, sub-bandsthat can expect to see higher interference from the macro cell, and thusallowing the small cell network to avoid transmissions in suchsub-bands. In another solution, the macro cell 522 can communicate asecond interference information, in particular, sub-bands for which themacro cell 522 expects to lower its own transmission level (i.e., to usea lower transmit power or duty cycle of transmissions), and thus whichcan then preferentially be used by the small cell network.

An example of the first interference information is the uplink HighInterference Indicator (HII), which is essentially a mechanismindicating an intention of neighboring cells to schedule high power inspecific bands, e.g., to imply transmission from cell-edge UEs. Anexample of second interference information is a downlink Relative NarrowBand Transmit Power (RNTP), defined in the LTE specifications, which isdefined as the expectation of a cell to reduce transmit power in certainRBs communicated to neighbor cells. While these two examples relate tothe frequency domain and are part of the ICIC specification in LTE,similar time domain examples exist and can also be employed. Forexample, information of Almost Blank Subframes (ABS) is an example ofsecond interference information of subframes in the time domain, wherethe macro cell does not transmit, or transmits at reduced power, whileinformation of Non-ABS subframes is an example of first interferenceinformation, i.e., of subframes in the time-domain that the macro cellwill likely transmit at full or higher power. Similar examples combiningtime and frequency information may also be used. In some cases, suchinformation need not be explicitly communicated by the macro cellnetwork, but instead measured by the small cell network and usedaccordingly.

In one exemplary implementation, a small cell network (e.g., the FFRmodules in the SN), use the above information in the design of the FFRpatterns for the individual small cells. In one method, they use theinformation to restrict or allow transmissions in a subset of edge-cells(or border cells) within the entire small cell network. Such bordercells may be defined based on the one or more of several measurementsbetween the small cells and the macro cell, and/or from the UEsconnected to the small cell or the macro cell. Examples of suchmeasurements include RSRPs, SNRs, or similar signal and interferencelevel measurements. RSRPs may be available at the UEs from macro cellmeasurements and at the small cells/RNs from performing sniffermeasurements of neighboring macro cells. In another method, the smallcell network may use the first and/or second interference information todefine cell-edge or cell-center bands for the FFR pattern design.

In a specific example, the first interference information ofhighly-interfered sub-bands could be used as a cell-center region (atleast in the border RNs, border clusters, or over the whole network) andthe second interference information of low-interfered sub-bands could beused as a cell-edge region (again, at least in the border RNs, borderclusters, or over the whole network). In one method, the cell-edge bandsassigned to a border or other RN within the cell-edge region of thecorresponding RN cluster (as defined in earlier embodiments) could bechosen to be the sub-band with the least expected macro cellinterference. In another specific example, the FFR algorithm mayconsider the detected macro cells at the border RN (or more generallyany RN), and the available first or second interference information ofthe corresponding macro cells, as bases in which to choose the cell-edgesub-band.

What has been disclosed are FFR schemes to improve the performance ofE-RANs, in particular targeting a better cell edge user experience.Advantages of certain implementations according to present principlesmay include one or more of the following. Certain implementations arescalable with clustering, and are expected to retain performance gainsfor large deployments. Certain implementations support autonomous FFRpattern selection and optimization based on RN topology, load, andchannels. Certain implementations allow inputs of KPIs and deploymentspecific parameters. Certain implementations minimize inputs to the RN'sscheduler to allow generally autonomous RN operation. Simulation resultsshow the effectiveness of the proposed schemes, which improve cell edgeusers throughput significantly, while maintaining user throughput inmany cases.

Several aspects of telecommunication systems will now be presented withreference to various apparatus and methods described in the foregoingdetailed description and illustrated in the accompanying drawing byvarious blocks, modules, components, circuits, steps, processes,algorithms, etc. (collectively referred to as “elements”). Theseelements may be implemented using electronic hardware, computersoftware, or any combination thereof. Whether such elements areimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system. By wayof example, an element, or any portion of an element, or any combinationof elements may be implemented with a “processing system” that includesone or more processors. Examples of processors include microprocessors,microcontrollers, digital signal processors (DSPs), field programmablegate arrays (FPGAs), programmable logic devices (PLDs), state machines,gated logic, discrete hardware circuits, and other suitable hardwareconfigured to perform the various functionalities described throughoutthis disclosure. One or more processors in the processing system mayexecute software.

Software shall be construed broadly to mean instructions, instructionsets, code, code segments, program code, programs, subprograms, softwaremodules, applications, software applications, software packages,routines, subroutines, objects, executables, threads of execution,procedures, functions, etc., whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise. Thesoftware may reside on non-transitory computer-readable media.Non-transitory computer-readable media may include, by way of example, amagnetic storage device (e.g., hard disk, floppy disk, magnetic strip),an optical disk (e.g., compact disk (CD), digital versatile disk (DVD)),a smart card, a flash memory device (e.g., card, stick, key drive),random access memory (RAM), read only memory (ROM), programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), aregister, a removable disk, and any other suitable non-transient mediafor storing or transmitting software. The computer-readable media may beresident in the processing system, external to the processing system, ordistributed across multiple entities including the processing system.Computer-readable media may be embodied in a computer-program product.By way of example, a computer-program product may include one or morecomputer-readable media in packaging materials. Those skilled in the artwill recognize how best to implement the described functionalitypresented throughout this disclosure depending on the particularapplication and the overall design constraints imposed on the overallsystem.

Variations of the above described systems and methods will be understoodto one of ordinary skill in the art given this teaching. For example,various measured parameters of UEs have been disclosed which bear onscheduling and other aspects, including RSRPs, CQI, QoS, and the like.Other parameters may also be employed, such as UE capabilities, apriority scheme for certain UEs, and the like.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method of optimizing frequency allocation in aradio access network (RAN) that includes a plurality of radio nodes(RNs) each associated with a cell and a services node (SN) operativelycoupled to the RNs, the method comprising: dividing the RNs in the RANinto a plurality of clusters of RNs; generating a fractional frequencyreuse (FFR) pattern for each cluster; allocating transmissionfrequencies to the RNs in each cluster in accordance with the respectiveFFR pattern that is generated for each cluster; estimating bit rates ofone or more user equipments (UEs) associated with each cell; performinga scheduling emulation within each cell using the estimated bit ratesfor the one or more UEs; and combining the results of the schedulingemulations for each cell to obtain a system-wide performance metric, andif the system-wide performance metric is less than a target threshold,then repeating one or more of the dividing, generating, allocating,estimating, performing, and combining steps, until the system-wideperformance metric meets or exceeds the target threshold.
 2. The methodof claim 1, wherein the estimating bit rates includes: calculating asignal to interference and noise ratio (SINR) for each UE; and using aSINR-to-bit-rate map to determine the bit rate for each UE.
 3. Themethod of claim 2, wherein the calculating a SINR includes using atleast one of a geometry and topology of the UEs to calculate the SINR.4. The method of claim 2, wherein the estimating bit rates furtherincludes improving the estimation of the bit rates by using channelquality indicator (CQi) data reported by the UE.
 5. The method of claim2, wherein the estimating bit rates further includes improving anestimation of the bit rates by using measurement data on an uplinkreference signal.
 6. The method of claim 2, wherein the system-wideperformance metric is selected from the group consisting of: cell packetthroughput, 5% cell edge user throughput, user throughput cumulativedistribution function (CDF), call drop ratio, call setup success rate,radio link failure rate, and handover delay.
 7. The method of claim 2,wherein following the system-wide performance metric meeting orexceeding the target threshold, allocating transmission frequencies tothe RNs in each cluster in accordance with the respective FFR patternthat resulted in the metric meeting or exceeding the target threshold,and performing a step of scheduling within each RN to allocatetransmission frequencies to the UEs served by the RN.
 8. The method ofclaim 2, wherein the system-wide performance metric includes thethroughput of a cell edge user, and wherein the repeating the generatingstep includes modifying the size of the center band until the throughputof the cell edge user meets or exceeds the threshold.
 9. The method ofclaim 1, wherein following the system-wide performance metric meeting orexceeding the target threshold, allocating transmission frequencies tothe RNs in each cluster in accordance with the respective FFR patternthat resulted in the metric meeting or exceeding the target threshold.10. The method of claim 9, comprising performing a step of schedulingwithin each RN to allocate transmission frequencies to the UEs served bythe RN.
 11. The method of claim 10, wherein the step of schedulingdirects cell edge users to high reuse bands and cell center users to lowreuse bands.
 12. The method of claim 11, wherein the step of schedulingis at least partially based on UE-specific information.
 13. The methodof claim 12, wherein the DE-specific information includes at least onemember selected from the group consisting of: CQI, load, and quality ofservice (QoS) parameters.
 14. The method of claim 10, wherein thesystem-wide performance metric includes the throughput of a cell edgeuser, and wherein the repeating the generating step includes modifyingthe size of the center band until the throughput of the cell edge usermeets or exceeds the threshold.
 15. The method of claim 10, wherein theestimating bit rates further includes improving the estimation of thebit rates by using channel quality indicator (CQi) data reported by theUE.
 16. The method of claim 10, wherein the estimating bit rates furtherincludes improving an estimation of the bit rates by using measurementdata on an uplink reference signal.
 17. The method of claim 1, whereinthe system-wide performance metric includes the throughput of a celledge user, and wherein the repeating the generating step includesmodifying the size of the center band until the throughput of the celledge user meets or exceeds the threshold.
 18. The method of claim 1,wherein the scheduling emulation includes proportional fairness (PF)scheduling emulation, round-robin scheduling emulation, or QOS basedscheduling emulation.
 19. The method of claim 1, wherein the estimatingbit rates further includes improving the estimation of the bit rates byusing channel quality indicator (CQi) data reported by the UE.
 20. Themethod of claim 1, wherein the estimating bit rates further includesimproving an estimation of the bit rates by using measurement data on anuplink reference signal.
 21. The method of claim 1, wherein thesystem-wide performance metric refers to a downlink transmission. 22.The method of claim 1, wherein the system-wide performance metric refersto an uplink transmission.
 23. The method of claim 1, wherein thetransmission frequencies are frequencies used for downlink transmission.24. The method of claim 1, wherein the transmission frequencies arefrequencies used for uplink transmission.
 25. The method of claim 1,wherein the system-wide performance metric is selected from the groupconsisting of: cell packet throughput, 5% cell edge user throughput,user throughput CDF, call drop ratio, call setup success rate, radiolink failure rate, and handover delay.